AI Agent Sandboxes Compared | Ry Walker Research

Key takeaways

E2B dominates the ephemeral sandbox market with 200M+ sandboxes and Fortune 100 adoption
Sprites challenges the ephemeral model with persistent VMs and instant checkpoint/restore
Alibaba's OpenSandbox brings a protocol-driven, open-source approach with multi-language SDKs and Docker/Kubernetes runtimes
Daytona offers the fastest creation (90ms) plus Computer Use support for browser/desktop automation

FAQ

What's the best sandbox for AI agents?

E2B for ephemeral execution, Sprites for persistent state with checkpoints, Daytona for Computer Use, Modal for GPU workloads, OpenSandbox for self-hosted Kubernetes-scale deployments.

Should I use ephemeral or persistent sandboxes?

Ephemeral (E2B) for stateless code execution and security. Persistent (Sprites, Daytona) when agents need to maintain state, installed packages, or context between runs.

Which sandbox supports GPUs?

Modal is the clear leader for GPU workloads. Others focus on CPU-based code execution.

What's the cheapest option?

All have generous free tiers ($30-50). For production, E2B and Sprites offer granular per-second billing. Modal bills per-second with GPU premiums.

Executive Summary

A new infrastructure category has emerged: sandbox platforms for AI agent code execution. These platforms solve the problem of "where should AI-generated code run?" with isolated, secure environments that agents can use to execute arbitrary code.

Market Leaders (13 platforms): AIO Sandbox, E2B, Microsandbox, Sprites, Daytona, Modal, Northflank, OpenSandbox, Quilt, Runloop, CodeSandbox SDK, ComputeSDK, Zeroboot

Key Findings:

AIO Sandbox (ByteDance-affiliated) takes an all-in-one approach — browser, shell, IDE, Jupyter, and MCP in a single Docker container with shared filesystem
E2B dominates with 200M+ sandboxes started and Fortune 100 adoption
Sprites (Fly.io) challenges the ephemeral model with persistent VMs and instant checkpoint/restore
Daytona offers the fastest creation (90ms) and unique Computer Use support
Modal leads for GPU workloads with serverless Python infrastructure
Northflank brings enterprise-grade VPC deployment with GPU support and flexible ephemeral/persistent modes
OpenSandbox (Alibaba) introduces a protocol-driven open-source approach with multi-language SDKs and Kubernetes-native scaling
Microsandbox (YC X26) is the only local-first microVM platform — libkrun isolation with network-layer secret injection that prevents credential exfiltration by design
Zeroboot achieves sub-millisecond spawns (0.79ms) via copy-on-write forking of Firecracker snapshots — 190x faster than E2B
The market is splitting into ephemeral (security-first) vs persistent (productivity-first) camps, with a new local-first camp emerging

Strategic Planning Assumptions:

By 2027, checkpoint/restore will become table stakes across all platforms
By 2028, Computer Use (browser/desktop) will be a standard feature, not a differentiator

Market Definition

AI agent sandboxes are isolated execution environments designed for running AI-generated or arbitrary code safely. They provide:

Isolation — Code runs in VMs or containers, separated from host systems
APIs/SDKs — Programmatic creation and management
Security — Protection against malicious or buggy code
Scalability — Ability to run thousands of concurrent sandboxes

Key distinction from cloud compute: These are purpose-built for agent use cases, not general application hosting. They optimize for fast creation, easy cleanup, and developer-friendly SDKs.

Comparison Matrix

Platform	Model	Creation	Persist	Checkpoints	GPU	Isolation
AIO Sandbox	All-in-one	~seconds	—	—	—	Docker
CodeSandbox	Persistent	~2s	✅	✅	—	microVMs
ComputeSDK	Abstraction	Varies	Varies	Varies	Varies	Varies
Daytona	Persistent	90ms	✅	—	—	Docker
E2B	Ephemeral	~150ms	—	—	—	Firecracker
Microsandbox	Local-first	~187ms	✅	—	—	libkrun microVMs
Modal	Serverless	~2s	—	—	✅	gVisor
Northflank	Both	~1s	✅	—	✅	microVMs/gVisor
OpenSandbox	Ephemeral	~seconds	—	—	—	Docker/K8s
Quilt	Ephemeral	~200ms	—	—	—	Namespaces
Runloop	Persistent	~2s	✅	✅	—	Custom
Sprites	Persistent	1-2s	✅	✅	—	Firecracker
Vercel AI Gateway	API Proxy	N/A	N/A	N/A	—	N/A
Zeroboot	Ephemeral	0.79ms	—	—	—	Firecracker CoW

Daytona also supports Computer Use (Linux/Windows/macOS virtual desktops). Vercel AI Gateway is an API proxy, not an execution sandbox — included for infrastructure completeness.

Product Profiles

AIO Sandbox

All-in-one Docker sandbox combining Browser, Shell, File, VSCode, Jupyter, and MCP in a single container. Built by the Agent Infra team (ByteDance-affiliated), used by UI-TARS-desktop.

3.4K+ GitHub stars, active development (150+ releases)
Unified file system — files shared across browser, shell, IDE, and Jupyter
Pre-configured MCP servers for browser, file, shell, and document processing
Python, TypeScript, Go SDKs
VNC + CDP browser control, VSCode Server, Jupyter Notebook
⚠️ Docker-level isolation (weaker than Firecracker/microVM)
⚠️ Self-hosted only, no managed cloud offering

Best for: Agent developers wanting a pre-configured all-in-one environment with MCP support. Development and trusted-code scenarios.

Pricing: Free (open source, Apache 2.0, self-hosted).

E2B

The market leader for ephemeral AI sandboxes.^[1] Open-source, Firecracker VMs, used by 88% of Fortune 100.

200M+ sandboxes started, 1M+ monthly SDK downloads
Firecracker microVMs (same tech as AWS Lambda)
Custom templates for pre-configured environments
Python, JavaScript, Go SDKs
⚠️ Ephemeral only — state destroyed after each session

Best for: Stateless code execution, security-sensitive workloads, high-volume eval pipelines.

Pricing: Free tier available. Usage-based billing.

Microsandbox

Local-first microVM sandboxes with network-layer secret injection.^[2] The only platform where credentials never leave your machine — not even to the sandbox itself.

YC X26 batch, founded by Stephen Akinyemi (Zerocore AI)
libkrun microVMs with hardware isolation (KVM/HVF) — sub-200ms startup
Secret injection: sandbox sees placeholders; real keys swapped at network layer only for verified TLS to allowed hosts
Programmable networking: DNS inspection, HTTP interception, domain allowlisting, DLP
Built-in MCP server for AI agent integration
Python, JavaScript, Rust SDKs; OCI-compatible images
Apache 2.0, 5K+ GitHub stars
⚠️ Experimental, self-hosted only (cloud "launching soon"), macOS + Linux only

Best for: Developers running AI agents locally who need maximum isolation and cannot tolerate secret exposure to any third-party infrastructure.

Pricing: Free (open source, self-hosted). Cloud offering coming soon.

Sprites

Persistent VMs with instant checkpoint/restore.^[3] Fly.io's answer to ephemeral sandbox limitations.

100GB persistent ext4 filesystem, backed by object storage
Checkpoint/restore in ~1 second (like git for the whole system)
Auto-sleep when idle, wake on demand — no charges when sleeping
Firecracker VMs with hardware-level isolation
⚠️ New product, ecosystem still maturing

Best for: Agents that need state between runs, experimentation with checkpoint/rollback, long-running dev environments.

Pricing: $0.07/CPU-hour, $0.04375/GB-hour memory. ~$0.44 for 4-hour Claude Code session. $30 trial credits.^[3]

Daytona

Fastest creation (90ms) with Computer Use support.^[4] Open-source, supports Linux/Windows/macOS virtual desktops.

Sub-90ms sandbox creation
Computer Use sandboxes — control virtual desktops programmatically
File, Git, LSP, and Execute APIs
SSH access, VS Code browser, web terminal for debugging
Open-source with self-hosting option
⚠️ Docker-based isolation (less secure than Firecracker)

Best for: Browser automation agents, Computer Use workloads, teams wanting open-source/self-hosted.

Pricing: Free tier. Usage-based for cloud.

Serverless Python with elastic GPU scaling.^[5] Built for ML/AI workloads, not just code execution.

Sub-second cold starts, instant autoscaling
GPU access (NVIDIA A100, H100) without quotas
Define infrastructure in Python code (no YAML)
Distributed filesystem for model loading
⚠️ Python-centric, less suitable for polyglot agents

Best for: GPU workloads, ML inference, training jobs, Python-native teams.

Pricing: $30/month free credits. Per-second billing for CPU, GPU, memory.

Northflank

Enterprise-grade microVMs with VPC deployment and GPU support.^[6] Running millions of sandboxes since 2021.

Sub-second cold starts with microVM or gVisor isolation
Deploy in their cloud OR your own VPC (AWS, GCP, Azure, Oracle)
Ephemeral or persistent — you choose per workload
GPU support (H100s at $2.74/hour, 62% cheaper than big clouds)
Persistent volumes up to 64TB, S3-compatible storage
Built-in CI/CD, autoscaling, and cost controls
Trusted by Sentry and 50k+ developers
⚠️ More full-stack platform than pure sandbox; may be overkill for simple use cases

Best for: Enterprise teams needing VPC deployment, GPU workloads with strong isolation, or hybrid ephemeral/persistent requirements.

Pricing: CPU $0.01667/vCPU-hour, Memory $0.00833/GB-hour. GPUs from $0.80-$3.14/hour. Free sandbox tier available.^[7]

OpenSandbox

Open-source sandbox platform from Alibaba with a protocol-driven architecture.^[8] Multi-language SDKs and Docker/Kubernetes runtimes for local dev through production scale.

Defines a "Sandbox Protocol" — standardized lifecycle + execution APIs
Multi-language SDKs: Python, Java/Kotlin, JS/TS, C#/.NET (Go on roadmap)
Docker runtime for local dev, high-performance Kubernetes runtime for distributed scheduling
Built-in Command, Filesystem, and Code Interpreter implementations
Network policy: unified ingress gateway with multiple routing strategies + per-sandbox egress controls
Rich examples: Claude Code, Gemini CLI, Codex CLI, OpenClaw, Playwright, Chrome, VNC desktop, VS Code, RL training
Apache 2.0 licensed, 2K+ GitHub stars
⚠️ Docker-based isolation (weaker than Firecracker); persistent storage still on roadmap

Best for: Teams wanting a self-hosted, protocol-driven sandbox platform with Kubernetes-native scaling and multi-language SDK support.

Pricing: Free (open source, self-hosted only).

Quilt

Open-source container infrastructure with inter-container communication.^[9] Self-hostable, built in Rust on Linux namespaces.

~200ms container creation
Inter-container communication (ICC) for networking between containers
Linux namespaces + cgroups isolation (lighter than Firecracker)
TypeScript SDK for agent integration
MIT/Apache-2.0 dual licensing, fully self-hostable
⚠️ Weaker isolation than Firecracker; early-stage product

Best for: Multi-container agent architectures, self-hosting, teams needing container networking.

Pricing: Open-source (self-hosted). Cloud offering in development.

Runloop

Devboxes with git-style state management.^[10] Snapshot and branch from disk state.

2x faster vCPUs on custom bare-metal hypervisor
100ms command execution
Snapshot and branch disk state (like git for sandboxes)
Built-in SWE-bench integration for agent evaluation
Repo connections with automatic environment inference
⚠️ Focused on agent benchmarking, less general-purpose

Best for: Agent development, SWE-bench evals, teams needing reproducible environments.

Pricing: Contact for pricing. Free trial available.

CodeSandbox SDK

Sandbox API with forking and snapshots.^[11] From the popular browser IDE company (now owned by Together AI).

Programmatic sandbox creation via SDK
Forking mechanism for A/B testing agents
Snapshots and hibernation
Resume development after inactivity
⚠️ Acquired by Together AI; product direction may shift

Best for: Teams already using CodeSandbox, web development agents, educational platforms.

Pricing: Free tier. Usage-based for scale.

ComputeSDK

Unified abstraction layer for sandbox providers.^[12] Write once, run on E2B, Daytona, Modal, Vercel, or CodeSandbox.

Hot-swappable providers — change via config, not code rewrites
Supports E2B, Daytona, Modal, Vercel, CodeSandbox
Free and open source (TypeScript)
"Terraform for running other people's code"
⚠️ Early stage (94 GitHub stars); abstraction may hide provider-specific features

Best for: Teams wanting multi-provider flexibility or avoiding vendor lock-in.

Pricing: Free (open source). You pay underlying providers directly.

Zeroboot

Sub-millisecond VM sandboxes via copy-on-write forking of Firecracker snapshots. 0.79ms p50 spawn, ~265KB per sandbox.

Copy-on-write fork of pre-loaded Firecracker memory snapshots
0.79ms p50 spawn latency — 190x faster than E2B
~265KB memory per sandbox vs ~128MB for E2B (~480x density)
1,000 concurrent forks in 815ms on a single machine
Real KVM hardware-enforced isolation per fork
Python and TypeScript SDKs, managed API available
⚠️ Working prototype, not production-hardened. No networking inside forks. Single vCPU only.

Best for: Batch code execution, agent evaluations at massive scale, high-throughput compute where networking isn't needed.

Pricing: Open source (Apache-2.0, self-hosted). Managed API in early access.

Vercel AI Gateway

Unified API proxy for 100s of AI models with budget controls and fallbacks.

Single API key accesses OpenAI, Anthropic, Google, xAI, and more
No markup on tokens — provider list prices
Automatic retries and fallbacks across providers
Built-in observability (traces, spend, latency)
BYOK (Bring Your Own Key) support
Sub-20ms routing latency

Best for: Teams using Vercel who want unified multi-provider access without managing infrastructure.

Pricing: $5/month free credit. Provider list prices for tokens (no markup).

Note: Vercel AI Gateway is an API proxy, not an execution sandbox. Included here as agent infrastructure.

Architecture Patterns

Isolation Technologies

Technology	Used By	Security Level	Performance
Firecracker	E2B, Sprites	⭐⭐⭐ Hardware-level	Fast
Firecracker CoW	Zeroboot	⭐⭐⭐ Hardware-level	Fastest
libkrun	Microsandbox	⭐⭐⭐ Hardware-level	Fast
gVisor	Modal, Northflank	⭐⭐ Kernel-level	Very Fast
Docker/K8s	AIO Sandbox, Daytona, OpenSandbox	⭐ Container-level	Fastest
Namespaces	Quilt	⭐ Kernel-level	Very Fast
Custom Hypervisor	Runloop	⭐⭐⭐ Hardware-level	Fast
microVMs	CodeSandbox, Northflank	⭐⭐⭐ Hardware-level	Fast

Firecracker (used by E2B, Sprites) provides the strongest isolation — the same technology AWS uses for Lambda. Each sandbox is a true microVM with its own kernel.

Ephemeral vs Persistent

The market is splitting into two philosophical camps:^[13]

Ephemeral (E2B model):

Fresh environment every time
Maximum security (no state leakage)
Simpler mental model
Must rebuild environment each session

Persistent (Sprites model):

State survives between runs
No rebuilding node_modules/packages
Checkpoint/restore for experimentation
Risk of state pollution

Fly.io argues that "ephemeral sandboxes are obsolete" for AI agents — Claude doesn't want a stateless container, it wants a computer.^[13] E2B would counter that ephemerality is a feature, not a bug, for security-sensitive enterprise deployments.

Local-first (Microsandbox model):

Runs on your machine, not in the cloud
Secrets never leave the host
No usage-based billing
Hardware isolation via local hypervisor
Limited to local compute resources

Microsandbox introduces a third camp: why send your code and credentials to someone else's infrastructure at all?^[14] For teams handling sensitive API keys, the local-first model eliminates an entire class of trust concerns.

Enterprise Feature Comparison

Feature	AIO Sandbox	CodeSandbox	ComputeSDK	Daytona	E2B	Microsandbox	Modal	Northflank	OpenSandbox	Quilt	Runloop	Sprites	Zeroboot
SOC2	—	✅	—	✅	✅	—	✅	✅	—	—	❓	✅ (Fly.io)	—
Self-hosting	✅ (only)	—	—	✅	✅	✅ (only)	—	✅ (VPC)	✅	✅	❓	—	✅
Open source	✅	—	✅	✅	✅	✅	—	—	✅	✅	—	—	✅
Custom templates	—	✅	—	✅	✅	✅ (OCI)	✅	✅	✅	—	✅	—	✅ (snapshots)
Team features	—	✅	—	✅	✅	—	✅	✅	—	—	✅	—	—
GPU	—	—	Varies	—	—	—	✅	✅	—	—	—	—	—
Computer Use	✅ (VNC+CDP)	—	Varies	✅	—	—	—	—	✅ (VNC)	—	—	—	—
Built-in MCP	✅	—	—	—	—	✅	—	—	—	—	—	—	—
Secret protection	—	—	—	—	—	✅ (network-layer)	—	—	—	—	—	—	—
Container networking	—	—	—	—	—	✅ (programmable)	—	✅	✅ (Ingress)	✅	—	—	—

Strategic Recommendations

By Use Case

Use Case	Recommended	Runner-Up
High-security enterprise	E2B	Northflank (VPC)
Persistent agent state	Sprites	CodeSandbox
GPU/ML workloads	Modal	Northflank
Browser automation	Daytona	—
Agent benchmarking (SWE-bench)	Runloop	E2B
Fastest creation time	Zeroboot (0.79ms)	Daytona (90ms)
Checkpoint/restore	Sprites	Runloop
Local-first / secrets on-host	Microsandbox	—
All-in-one dev environment	AIO Sandbox	Daytona
Open source preference	E2B	OpenSandbox
Cost optimization (idle)	Sprites	Modal
Inter-container networking	Quilt	Northflank
VPC/BYOC deployment	Northflank	Daytona (self-hosted)
Multi-provider flexibility	ComputeSDK	—

By Team Profile

Security-first enterprise team: → E2B (ephemeral, Firecracker, SOC2) or Northflank (VPC deployment)

AI agent startup iterating fast: → Sprites (persistence saves rebuild time, checkpoints for experimentation)

ML/AI team needing GPUs: → Modal (serverless Python) or Northflank (VPC + full infrastructure)

Building Computer Use agents: → Daytona (only option with desktop automation)

Self-hosted Kubernetes-native team: → OpenSandbox (protocol-driven, multi-language SDKs, K8s scheduling)

Security-paranoid team (secrets must stay on-host): → Microsandbox (local-first microVMs, network-layer secret injection)

Running agent evaluations: → Runloop (built-in SWE-bench) or E2B (scale)

Enterprise needing VPC/BYOC: → Northflank (AWS, GCP, Azure, Oracle support with full platform)

Market Outlook

Near-Term (2026)

Checkpoint/restore spreading beyond Sprites/Runloop
E2B maintaining market share lead on ephemeral
Daytona growing in Computer Use segment

Medium-Term (2027)

Persistent vs ephemeral distinction blurring (both become options)
Computer Use becoming standard feature
GPU support expanding — Northflank already challenging Modal's monopoly

Long-Term (2028+)

Category may consolidate around 2-3 leaders
Integration with agent orchestration platforms (Tembo, etc.)
Specialized sandboxes for specific agent types

Bottom Line

13 platforms serve the AI agent sandbox market with distinct approaches:

Platform	Best For	Key Differentiator
AIO Sandbox	All-in-one dev environment	Browser+Shell+IDE+MCP in one container, ByteDance-backed
CodeSandbox	Web dev agents	Forking, established ecosystem
ComputeSDK	Multi-provider flexibility	Unified API across E2B/Daytona/Modal/etc.
Daytona	Computer Use and speed	90ms creation, Linux/Win/macOS desktops
E2B	Ephemeral execution at scale	200M+ sandboxes, Fortune 100 adoption
Microsandbox	Local-first secret protection	libkrun microVMs, network-layer secret injection
Modal	GPU workloads	Elastic GPU scaling, Python-native
Northflank	Enterprise VPC + GPUs	BYOC deployment, microVM/gVisor isolation
OpenSandbox	Self-hosted K8s-scale	Protocol-driven, multi-language SDKs, Alibaba-backed
Quilt	Multi-container agents	Open-source, inter-container networking
Runloop	Agent development/evals	SWE-bench integration, disk snapshots
Sprites	Persistent state with checkpoints	Object-storage durability, instant restore
Zeroboot	Ultra-high-throughput batch execution	0.79ms spawn, ~265KB/sandbox, CoW forking

The market is early and growing fast. E2B has the adoption lead, but Sprites and Daytona are pushing the boundaries on what sandboxes can do. ByteDance's AIO Sandbox takes a different approach entirely — instead of optimizing one dimension, it bundles everything into a single container with a shared filesystem, betting that integration simplicity matters more than isolation strength for many use cases. Modal leads GPU workloads but Northflank is challenging with cheaper H100s and VPC deployment. Alibaba's OpenSandbox brings a credible open-source, protocol-driven alternative for teams wanting full control at Kubernetes scale. Microsandbox (YC X26) introduces a new axis — local-first microVMs where secrets never leave the host — that may prove compelling as AI agents handle increasingly sensitive credentials. The winners will be determined by which model — ephemeral, persistent, or local-first — proves better for production AI agents, and whether enterprises prefer pure sandboxes or full-stack platforms.

Research by Ry Walker Research

Disclosure: Author is CEO of Tembo, which may integrate with sandbox platforms for agent execution.

Sources